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Chipp

Chipp

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Swing trading a procative SMSF with high aspirations . Trades , maybe some stock screening , trade management , systems to extract max value and whatever else comes to mind . Not sure i will keep this that uptodate but i'll try .. Few trades on the radar atm .
 
Chipp said:
Swing trading a procative SMSF with high aspirations . Trades , maybe some stock screening , trade management , systems to extract max value and whatever else comes to mind . Not sure i will keep this that uptodate but i'll try .. Few trades on the radar atm .
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Looking forward to it,
Like your idea of knowing some non Aussie fund can play the big share dividends wo cashing it, just using the SP.
Interested in knowing more about your methods and point of views.
Btw , are you aussie ?
 
Ok from my scans have a few prospects WOW SHL TCL , will review key metrics today . I think WOW is a lock . I got few more Speculative ones are on the list but will avoid them in here for now . TLS came up on scan but although i think it will make gains i see them as limited .
 
Chipp said:
Ok from my scans have a few prospects WOW SHL TCL , will review key metrics today . I think WOW is a lock . I got few more Speculative ones are on the list but will avoid them in here for now . TLS came up on scan but although i think it will make gains i see them as limited .
Click to expand...

I think that you are correct with WOW. They are getting a hammering from the media and governments at the moment, which has contributed to their SP drop. However, they are a strong business with large market share, and customers still believe in them.
 
Chipp said:
A single data " event " once a year in market 22% . Too good to be true ? Maybe . Can i fill in most of a year with ' events ' like this ? yes i think i can
View attachment 183551
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Mr Chipp, just to confirm I'm reading your data correctly:

So 24 trades in total. 22 winning trades. 1 breakeven. 1 loss.
Average winner $57K
Average loser $44K

What not disclosed: the instrument or instruments used (stocks, futures, etc).
Type of data 'event' traded.

The type of potential data event are myriad, so I won't try to list examples, but could include from micro through to macro.

Given the very high win ratio, balanced against the on average 1:1 R/R (approximately) your edge or current lacuna will (most likely) be a mathematical observation.

There have historically been some very interesting math traders. Mr Simmons (now passed) was a good example in the recent past.

I note from another thread that you have suspicions that this anomaly is possibly being noticed by others and could erode. I find this aspect interesting as it indicates an early entry into a high (very) probability outcome, which rather tallies with the description of 'data events'.

jog on
duc
 
ducati916 said:
Mr Chipp, just to confirm I'm reading your data correctly:

So 24 trades in total. 22 winning trades. 1 breakeven. 1 loss.
Average winner $57K
Average loser $44K

What not disclosed: the instrument or instruments used (stocks, futures, etc).
Type of data 'event' traded.

The type of potential data event are myriad, so I won't try to list examples, but could include from micro through to macro.

Given the very high win ratio, balanced against the on average 1:1 R/R (approximately) your edge or current lacuna will (most likely) be a mathematical observation.

There have historically been some very interesting math traders. Mr Simmons (now passed) was a good example in the recent past.

I note from another thread that you have suspicions that this anomaly is possibly being noticed by others and could erode. I find this aspect interesting as it indicates an early entry into a high (very) probability outcome, which rather tallies with the description of 'data events'.

jog on
duc
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well IF i was doing that well ( and i am NOT ) i would be keeping it secret ( or patented ) too

a very nice strike rate

hopefully @Chipp can do that fairly consistently
 
ducati916 said:
What not disclosed: the instrument or instruments used (stocks, futures, etc).
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These types of trades can literally be any instrument . As long as it ticks the boxes required
ducati916 said:
Type of data 'event' traded.
Click to expand...
The data events also can be a myriad of things
whatever it might be there is almost always some logic behind it that effectively creates a temporary price imbalance , a shortlived change in supply /demand

ducati916 said:
Given the very high win ratio, balanced against the on average 1:1 R/R (approximately) your edge or current lacuna will (most likely) be a mathematical observation.
Click to expand...
Its all driven by maths and messurements of tangible definable metrics , no squilly line generic TA BS ( macd rsi fibonnaci etc )
I write all my own coded measuring instruments , The only builtin i really use is ATR but even then i modify it to suit my purposes .
ducati916 said:
There have historically been some very interesting math traders. Mr Simmons (now passed) was a good example in the recent past.
Click to expand...
Jim Simons is/was a huge inspiration to me at the beginning of my statistical/quant style journey
ducati916 said:
I note from another thread that you have suspicions that this anomaly is possibly being noticed by others and could erode. I find this aspect interesting as it indicates an early entry into a high (very) probability outcome, which rather tallies with the description of 'data events'.
Click to expand...
Much of the work ive previously done is now becoming available to mainstream traders/quants and it does concern me edges will be eroded . But that said few if any actually know the logic behind much of it and actually know how to measure/test and validate it . And for that i am grateful but ultimately i think some ideas have a limited lifespan . I am always searching for new and unique ideas though . A lot of my ideas come from observing naked charts and seeing a ' heartbeat ' to price action which from there i will measure and define patterns in both movement of price and time between the ' beats ' . Its a deep rabbit hole , if you dont look you dont find . Many try and polish an old turd , i try and find a new turd ;)

Also the ability to code is a necessary part of this but the ability to turn a pattern into maths is the real skill required . Also a background in discretionary trading helps a lot in visualizing a pattern most cannot see . A lot of price is noise , for me i am most interested in the turning points where volatility is raised . If there logic behind it we have the beginnings of an edge .
 
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Here is a brief summary of what's in the book:

Highlights mine:

I eagerly read through the entire book so that I could assess how different his quantitative approach is against the AlphaCovaria System I have been relying on as mentioned above. I am so grateful for Mr. Zuckerman who dug out so many details about how Simons’s models have been built. Here is a summary of what I have learned from a quantitative trader’s perspective:

(1) First, a little background. While at IDA during his earlier career, Simons and his colleagues wrote a research paper that determined that markets existed in various hidden states that could be identified with mathematical models. At IDA, they built computer models to spot "signals" hidden in the noise of the communications of the United States' enemies. This was the precursor to Simons’s later persistent pursuit to testing the approach in real life.

(2) Performance-wise, Simons has been the most successful one in trading, given the performance comparisons of this list: Jim Simons (Medallion) 39.1%, George Soros (Quantum Fund) 32%, Steven Cohen (SAC) 30%, Peter Lynch (Magellan Fund)29%, Warren Buffett (Berkshire Hathaway) 20.5%, and Ray Dalio (Pure Alpha) 12%. One of the factors that Simons could succeed so much is that he is a strongly principled person with a strong belief in "Work with the smartest people you can, hopefully, smarter than you... be persistent, don't give up easily." So he is not only a great mathematician but also a great visionary and business manager.

(3) Their model dev process: By 1997, Medallion's staffers had settled on a three-step process to discover statistically significant moneymaking strategies, or what they called their trading signals: (1) Identify anomalous patterns in historic pricing data, (2) make sure the anomalies were statistically significant, consistent over time, and nonrandom , and (3) see if the identified pricing behavior could be explained in a reasonable way.

(4) Trading frequency: Medallion made between 150,000 and 300,000 trades a day, but much of that activity entailed buying or selling in small chunks to avoid impacting the market prices.

(5) Data granularity: They use five-minute bars as the ideal way to carve things up. Their data hunter Laufer's five-minute bars gave the team the ability to identify new trends, oddities, and other phenomena, or, in their parlance, nonrandom trading effects.

(6) Holding period: Medallion still held thousands of long and short positions at any time. Its holding period ranged from one or two days to one or two weeks. The fund did even faster trades, described by some as high-frequency, but many of those were for hedging purposes or to gradually build its positions. Renaissance still placed an emphasis on cleaning and collecting its data, but it had refined its risk management and other trading techniques.

(7) Their performance as measured by Sharpe ratio. 1990s, Medallion had a strong Sharpe ratio of about 2.0, double the level of the S&P 500. But adding foreign-market algorithms and improving Medallion's trading techniques sent its Sharpe soaring to about 6.0 in early 2003, about twice the ratio of the largest quant firms and a figure suggesting there was nearly no risk of the fund losing money over a whole year. No one had achieved what Simons and his team had-a portfolio as big as $5 billion delivering this kind of astonishing performance. In 2004, Medallion's Sharpe ratio even hit 7.5, a jaw-dropping figure. Medallion had recorded a Sharpe ratio of 2.5 in its most recent five-year period, suggesting that the fund's gains came with low volatility and risk.

(8) Their portfolio composition. They started with commodity, bond, and currency, but later expanded into equities, which became the major source of profits after many years of efforts.

(9) Does Simons strictly stick to their models? In general, yes, but he made calls when he saw models were malfunctioning due to extreme market conditions.

(10) How have their models worked under various market conditions? Their models are mostly neutral, which was made possible by making quick trades only to eliminate unforeseeable events. They claimed that they could make models that would work with long-term investments, but it seems that they have not done so.

(11) What is the most secret juice with their models? Medallion found itself making its largest profits during times of extreme turbulence in financial markets. They believed investors are prone to cognitive biases, the kinds that lead to panics, bubbles, booms, and busts. "We make money from reactions people have to price moves." They look for smaller, short-term opportunities-get in and get out. The gains on each trade were never huge, and the fund only got it right a bit more than half the time, but that was more than enough. "We are right 50.75 percent of the time... but we're 100 percent right 50.75 percent of the time," Mercer told a friend. "You can make billions that way."

(12) How long was their learning curve? Simons spent 12 full years searching for a successful investing formula, without much success until he and Berlekamp built a computer model capable of digesting torrents of data and selecting ideal trades, a scientific and systematic approach partly aimed at removing emotion from the investment process.

(13) Size of their computing infrastructure. On page 248, it says their computer room was the size of a couple of tennis courts. I arrived at a guestimate that they might have about ~13,000 servers, computed like this: 2x78x27 (two tennis courts) x 0.6 (total area occupied by racks) / (2x4 (rack area)) x 40 (servers per rack) = 12,636. This should not be too far away from what they have.

So some commonalities that I have extolled:

Volatility is THE place to make money (with relatively low risk)
Maintain a largely neutral market balance, hedging exposure.
Non-linear outcomes (not in the text, but in the video).


New:

Using 5min bars.
# of trades
# of computers engaged.
Anomalous market patterns, events.

A second review:

Highlights mine:

1. I’m not sure the title does justice to the book or to participants. There’s no doubting the obscenely amazing performance of the Medallion funds. Mr. SImons, as founder and CEO, certainly has earned a large place in financial history. But while the book does a terrific job communicating Mr. Simons’ early work as a cryptographer & his academic achievements in respect of geometers etc., it seems to describe a man who had a vision for how the market might be solved & drove the funding/infrastructure for realization - but not a man who actually developed the specifics of the fund’s model. It really seems to be several of the other key characters who poured endlessly over pricing history, identified & tested anomalies and wrote the algorithmic codes (beginning with commodities & fixed income, equities later on).

2. The author does a very good job, IMHO, of discussing concepts like factor investing, statistical arbitrage, paired trades, hedges, market neutral, etc. And he takes the time to nicely reference some of the underlying math for those who have the interest, touching on concepts ranging from differential equations to mean reversion to Brownian motion to embedded Markov processes. The author doesn’t purport to try and teach readers how they might use those ideas - appropriately so - but it’s meaningful perspective.

3. Not surprisingly, there’s a dichotomy re “how” the market was “solved.” There won’t be much new here for traders. At the broadest level of generality, certain pricing anomalies were identified & incorporated into algorithms that turned the raw data into trading signals. Harnessing computing power, the fund trades a ton, such that it doesn’t need to make much on each trade and only needs to get it right a bit over half the time - returns are then amplified by liberal employment of leverage; the systematic model is trained - application of machine learning - to continue to improve precision on its own and to determine trades/positions. Beyond that, though - & it shouldn’t be folks’ expectation- the book doesn’t go granular on the model’s inputs. It can’t and doesn’t give away the particulars of the black box. The author should be credited for his tackling of the funds’ initial problems with slippage and for reporting on how the funds had no choice but to move into equities in order to attain such massive AUM. Also great history on early and superior efforts to obtain/recreate pricing data & good discussion of the core fund’s preference for extremely short holding periods.

4. There’s some pretty riveting investing history here, ranging from early developments in technical analysis to the long and steady rise of fundamentals-based investing to the profound skepticism with which systematic quant trading was treated for an exceptionally long time.

5. The narrative is at times beautiful , at others choppy and abrupt. Probably too many cases of basically “the fund was in trouble” to “the fund was thriving”. It’s like, “oh, that’s good”

6. In terms of personal biography, my understanding is that Mr. Simons is intensely private - under those constraints, the author does well in tracing his life and career, though for me, a truly strong and well developed portrait remains elusive. The author comes closer to that mark in telling the stories of several of the other key participants in the firm’s rise over time.

7. Later in the book, a ton of space is devoted to Robert Mercer’s public politics and how it impacted the firm. I thought it was interesting stuff, but some may find it loses focus, e.g. there’s quite a bit on Rebekah Mercer that just doesn’t have much relation to the core story.

This was an ambitious endeavor and Mr. Zuckerman should be credited for that. As personal biography, it’s s fine effort. As financial history, I’d characterize it as informative, accessible and entertaining. But I’m not sure I’d say it’s of huge importance. The telling of the story isn’t, in my view, likely to have any real impact on the methods and practice of finance. But for finance junkies, there’s a ton of on point info, perspective, teaching and fun. Thanks much to the author.


Last one:

Highlights mine

This summer I plowed through his most recent work "The Man Who Solved the Market" as I became deeply engrossed in the subject matter and decided that it wasn't "above my head or pay grade at all". I must admit that I didn't just polish it off immediately when I first got it as I was a bit intimidated as initially he goes through the bios of some of the initial mathematicians/scientists working for/with Simons (Baum, Ax, Berlekamp, Laufer, etc) and briefly discusses concepts such as kernels, stochastic differential equations, hidden Markov models, etc. which just made my eyes glaze over as I am an opportunistic, discretionary trader, and not quant/systematic. Even in Greg's acknowledgements he writes he never got past pre-calculus in HS (me just calculus II in college--and that was >15 years ago)! So that stuff was admittedly dense and arcane for a neophyte wishing to engage in that subject matter, yet I like and appreciate the way he explained those complex mathematical terms both in a way that a layman like myself could understand it, but also not in a way so as to not insult the intelligence of other readers who may be quants who are intimately familiar with this subject material. But it was a well done book that gave the reader as close a look as an outsider is going to get as to how RenTec does what it does.

I especially enjoyed these 2 quotes: "What you're really modelling is human behavior. Humans are most predictable in times of high stress--they act instinctively and panic. Our entire premise was that human actors will react the way humans did in the past...we learned to take advantage." & "We make money from the reactions people have to price moves."

It started off with a great bio of Simons (I especially liked the part about how him and his friend were traveling cross-country following college, and they stopped for a swim in MS, and upon asking why there were no African-Americans there, was told they weren't allowed--and how this was a seminal moment in shaping his future political beliefs as well as his philanthropy in both autism and math education). And then of course Medallion itself, I mean wow, absolutely incredible returns, especially after Bob Mercer and Peter Brown join (say what you will about Mercer--and Greg includes some of these salacious details--but apparently he got the job done. I enjoyed his quote, "We get 50.75% of our trades right, but of that 50.75%--we get 100% of that right. And you can make billions that way." Now that's a baller quote (regardless of the politics, with which of course I abhorrently disagree). And the returns speak for themselves: 66.1% gross, 39.1% net over the 30 years from 1988-2018 (I can only imagine how much it cleaned up 1H20 during the COVID crisis.)

So it was an outstanding job straddling the fine line between making this book both accessible, but still very high-level and informative (just as his two other books "The Greatest Trade Ever" and "The Frackers" are, as well his work for WSJ). I highly recommend it.

Interesting, obviously I have ordered the book.

jog on
duc
 
Tools for a quant trader take any guess work away using scientific approaches . 3d dot plot distribution model is one of these tools , possible to test a billion parameter combinations inside an afternoon running a series of explorations . Naturally takes a while to get to these levels .


ScreenShot618.jpg
 
ducati916 said:
I especially enjoyed these 2 quotes: "What you're really modelling is human behavior. Humans are most predictable in times of high stress--they act instinctively and panic. Our entire premise was that human actors will react the way humans did in the past...we learned to take advantage." & "We make money from the reactions people have to price moves."
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100% " The past is the future " when it comes to anything driven by human psyche . Precedent at extremes of this Psyche are a guide to what happens next .

Jim Simons averaged 66% return over decades with no losing years in Medallion Fund , and that sounds incredibly daunting . but break that down into blocks of the bars need to acheive that 4.4% a month / 1% a week / 0.2% a day . If you are trading ASX200 futures that is 16 points a day net returns at current index pricing , all of a sudden it's not quite so daunting . Not saying it's easy of course otherwise everyone would be doing it . Its possible to make that 16p net by 12am most days
 
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Chipp said:
Tools for a quant trader take any guess work away using scientific approaches . 3d dot plot distribution model is one of these tools , possible to test a billion parameter combinations inside an afternoon running a series of explorations . Naturally takes a while to get to these levels .


View attachment 183624
Click to expand...


LOL.

Makes my model look like 1+1 = 2

jog on
duc
 

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